1. Technological Field
This invention pertains generally to image processing, and more particularly to semi-automatic color image segmentation.
2. Background Discussion
Image segmentation is a process of partitioning a color image into regions. The simplest case involves separating a foreground object having known properties from the background.
Different approaches have been attempted for segmenting the image area of an object from its surroundings. The simplest approach is perhaps that based on thresholding. For instance, given the user-selected point, the segmented object would be the collection of the adjacent pixels having similar color values to the color value (the centroid) at the user-selected point in a certain range. This method functions for only single-colored objects, otherwise the user will be required to select multiple centroid values, and select color ranges manually for segmentation of the multi-colored objects, to get all elements of the object selected.
A ‘grab-cut’ approach is another color image segmentation method, which is a modified version of the ‘graph-cuts’ method for gray-scale image segmentation, where the gray ranges for the two classes, foreground and background, are automatically chosen by variance of the foreground or background pixels. In grab-cut, each foreground or background class is further split into several sub-classes to deal with multi-colored objects. Each sub-class is represented by the centroid color value and the co-variance matrix, due to the vector valued pixel (i.e., red, green, and blue). In the classical sense, such a model is referred to as a mixture model.
Often, the probability density function of the color pixels is approximated by a weighted linear combination of Gaussian functions parameterized with the centroids and the covariance matrices, and it is then called Gaussian mixture model (GMM). Using GMM, an unlabeled pixel is classified into the nearest classes where the distance between the unlabeled pixel and each class is computed with the centroid and covariance matrix. The approach is nothing but the classical Bayes classifier. The novelty of grab-cut is the correction term (or the smoothness term) for the distance measure. An analysis of the local gradients around the unlabeled pixel is taken into account, and that is the one reason why grab-cut performs better than the threshold approach. However, grab-cut requires the user to input a box around the object of interest. This box in fact provides not only the object's position but also the rough size. Further user inputs are required for minor correction of the segmented object.
However, as has been described above, proper image segmentation typically involves considerable user input for arriving at a proper segmentation results.
Accordingly, a need exists for an image segmentation method which is sufficiently accurate to allow semi-automatic image segmentation, while overcoming shortcomings of prior segmentation methods.